The present invention relates sensing and detection of electrical or other characteristics associated with defective or unauthorized items in a supply chain using multiple detection and data system modalities. Detection and/or part/system attribute or data modalities can include multiple varieties of particular testing methods or procedures, data systems containing authorized/defect/unauthorized attribute data for individual or classes of items, as well as a variety of different sensory systems that can used for testing items, and/or related data collections containing electrical or other characteristics associated with items of interest. Defects or unauthorized status can include parts that do not conform to their specifications, are not authorized by an original equipment manufacturer, a case where a used part is being passed off as a new part, or a case where a part or component has been subjected to one or more damage or stress events exceeding acceptable limit such as electrostatic discharge (ESD) events. System defect or supply chain problem detection is increasingly more difficult given large volumes, difficulty in accessing parts in an assembly, and different sizes, shapes, and input/output structure, particularly for mass produced parts or defect detection for parts that have left a factory. Thus, there is a need to improve electronic system supply chain defect detection capabilities which can be used at any stage in a supply chain.
A common problem with existing methods of acquisition and comparison of parts in a supply chain is that they are generally not good at accounting for normal manufacturing process variations, which can vary with device lots and foundries. Testing systems also tend to focus on a single stress indicator, such as input/output (I/O) shift due to ESD. Thus such testing systems or approaches do not represent comprehensive evaluation methods nor do they address cases where a part or system is non-conforming to its specification or advertised status e.g., new/not-used/damaged to a small extent. Existing systems also do not provide a combination screening capability which includes ability to screen parts for both aging and environmental stress in addition to other factors such as physical characteristics. Existing systems also do not combine many different data sets to create a comprehensive set of data using simpler and less costly methods and thus provide a reliable and significantly accurate system which permits high capacity or high speed testing system. An embodiment of the invention can provide testing in different locations of a supply chain for parts in different part, end use, or packaging configurations.
One embodiment of the invention uses multiple test detection and data collection/input modes or modalities coupled with one or more decision engines such as neural networks, image recognition, statistical correlation tools, and decision trees, which can incorporate various learning processes. Another embodiment can also include a data collection system with one embodiment including electromagnetic (EM) sensors and data collection inputs adapted to sense test data and input the data to an embodiment of the multiple mode analysis using, e.g, a decision engine to evaluate a device under test (DUT) system. For example, an embodiment of the invention can incorporate integration of multiple EM sensors as well as data inputs and in synchronization with DUT stimulation for the purpose of producing device unique EM signatures accompanied by a decision engine, including a neural engine, to provide a variety of novel embodiments of the invention to meeting a variety of supply chain item defect or unauthorized item detection needs.
An exemplary embodiment can apply a decision engine to multiple electrical characteristic modalities data sets for the purpose of determining a probability that a microelectronic device is unauthorized, does not meet specification(s), or is defective. Inputs to an exemplary decision engine can include a variety of potential data sets that can be evaluated. The additional information obtained in applying multiple data sets in combination with a sensor system that can be used with a wide variety of DUTs, both in a factory and elsewhere, will allow a much more accurate probability assessment of DUTs. Testing systems can also use various methods for measuring different stressors that would indicate a part has, for example, been previously used or stressed (thus is unacceptable or does not meet specification(s)), such as experiencing an ESD damage event.
An exemplary stimulus including multiple electrical characteristic testing regimes could be applied in such a way as to produce one or multiple device dependent signatures, including signatures associated with known good devices and known bad devices, which correlate signature information with DUT testing results using same or similar tests employed in creating the device dependent signatures which are useful in determining a probability that a device has a defect, improper part installed, or has otherwise experienced environmental stress. An exemplary implementing system can include an artificial intelligence (AI) or expert system rule base which runs if/then statements against DUT test results to perform correlation tasks.
An exemplary system can include a neural network or other AI system which permits an initial identification or flagging of a suspect part or system based on a first application of the invention. A result of a manual inspection of the identified or flagged part can then be input into the invention to update a reliability data field associated with one signature or a pattern of signatures using, for example, a neural network type learning system.
An embodiment of a learning system can update a device signature database which is used to determine a probability of accuracy relative to flagged or identified suspect part. Device signatures can include data sets such as, for example, failure/defect/counterfeit indicators and non-failure/defect/counterfeit indicators along with relative weights (denoting relative strength of the indicator) assigned to each indicator which are used when factors are combined to create a composite acceptance/reject determination and probability indicator.
Known bad and known good data signatures for specific devices or parts as well as classes of devices or parts can be created. Counterfeit detection indicators can include factors associated with ESD, mischaracterization, ageing, factory setting data defaults in memory components, predicted built in test (BIT) results, material composition, structural features, infrared signatures associated with different operating modes, vibration/mechanical stress, quality factors, overclocking testing (with and without artificially induced ageing and overclocking testing (both to spec and to max performance failure) at different age equivalent points), and impedance testing. Tests which identify bad or unacceptable parts or systems based on a specific or group of indicators can generate new data signatures data sets which are then associated with a known bad data set with an increased relative weight or a known-bad indicator which are used in correlation with test results from a DUT using same or similar tests used to formulate data signatures and then factored into a composite acceptance/rejection determination and probability indicator formulation.
An exemplary EM apparatus may include a positioning system, switch matrix, power combiner, switch and EMI shielding to minimize stray EMI signals. An exemplary embodiment can also combine various probe types, such as E-field, and H-field probes of varying bandwidths, as well as visual, infra-red, etc in an integrated manner.
Additional features and advantages of the present invention will become apparent to those skilled in the art upon consideration of the following detailed description of the illustrative embodiment exemplifying the best mode of carrying out the invention as presently perceived.